Why Healthcare Companies Need a Platform Approach for Building AI Solutions

Artificial Intelligence (AI) holds transformative potential for healthcare, promising to improve patient outcomes, streamline operations, and reduce costs. Yet, many healthcare organizations struggle to implement AI in a scalable, secure, and efficient manner. A key reason is the lack of a coherent, strategic approach when it comes to the architecture on which these AI solutions are built.

In this blog, we will explore why healthcare organizations must consider taking a platform-based approach to AI and how different implementation strategies can address their unique needs and challenges.

The Need for an AI Platform in Healthcare

Data Complexity

Healthcare data is highly diverse, encompassing structured data (e.g., EHRs, lab results, demographic information), unstructured data (e.g., clinical notes, imaging), and real-time streams (e.g., wearables, patient monitors). A platform approach enables organizations to ingest, normalize, and manage this data at scale in a standardized way, ensuring accuracy, security, and compliance from the ground up.

Regulatory Compliance & Security

Healthcare is one of the most heavily regulated industries, with stringent requirements around patient data privacy (HIPAA), security, and ethical use of AI. A platform approach allows organizations to:

• Implement consistent security controls across all AI applications
• Centralize audit trails and monitoring for regulatory inspections
• Ensure appropriate patient consent management
• Maintain clear data lineage and provenance
• Apply consistent validation processes for AI models
• Facilitate FDA compliance for AI/ML-based medical devices (SaMD)
• Standardize documentation for regulatory submissions

Rather than recreating compliance frameworks for each AI initiative, a platform provides built-in guardrails that ensure all applications adhere to regulatory requirements.

Scalability & Performance

As AI use cases in healthcare expand — from diagnostics to population health management and beyond — organizations need infrastructure that scales. A platform can support multiple AI applications across departments, enabling reusability of models, code, and data pipelines.

Collaboration & Reusability

AI initiatives in healthcare typically involve cross-functional collaboration (data scientists, clinicians, IT teams, compliance officers, etc.). A platform fosters centralized governance, version control, and consistent processes. This reduces silos and accelerates time-to-insight by reusing existing components — data connectors, feature stores, and pre-trained models.

Vendor & Technology Flexibility

With a well-defined AI platform, healthcare organizations can plug in new tools, frameworks, or libraries more easily. This technology-agnostic approach helps avoid vendor lock-in, fostering an ecosystem of innovation that can quickly adapt to new breakthroughs in AI.

Benefits of Taking a Platform Approach

Unified Data Strategy

A platform approach centralizes data management, creating a single source of truth that improves data quality and accessibility for AI applications. This is particularly valuable in healthcare, where data is often fragmented across multiple systems.

Faster Innovation & Deployment

By having standardized components — data pipelines, API integrations, model-serving infrastructure — data scientists and clinicians can focus on solving medical problems instead of reinventing the wheel for each new AI initiative.

Consistency in Models & Workflows

A centralized platform ensures that the same data standards, security protocols, and processes are followed across the organization. This consistency helps reduce errors, maintain regulatory compliance, and ensure predictable performance.

Cost Efficiency & Resource Optimization

While a platform may require an initial investment, the economies of scale realized over time — both in terms of model development and infrastructure usage — often outweigh the costs. Reusing existing models and data pipelines prevents duplication of effort.

Enhanced Collaboration & Knowledge Sharing

AI platforms typically provide collaboration features: version control for models, shared workspaces for data and code, and dashboards for performance metrics. This transparency fosters cross-team learning and drives better outcomes faster.

Improved Patient Outcomes & Insights

By leveraging a broad set of shared data and AI capabilities, organizations can uncover deeper insights. From personalized treatment recommendations to predictive analytics for hospital readmissions, a platform approach empowers the type of large-scale, data-driven decision-making that improves patient care and operational efficiency.

Consequences of Not Taking a Platform Approach

Siloed and Redundant Efforts

Without a unified platform, different teams or departments may independently build AI solutions that duplicate efforts or use inconsistent data sets and methods. This leads to wasted resources and inconsistent results.

Prolonged Time-to-Value

Implementing each AI solution from scratch — infrastructure, data pipelines, compliance checks, etc. — is time-consuming. Without a platform, organizations often lose competitive advantage by not rolling out AI projects swiftly.

Data Silos and Fragmentation

Point solutions often create isolated data repositories, limiting the availability of information for comprehensive analysis and reducing the potential value of AI applications.

Compliance Vulnerabilities

Without a unified platform, regulatory compliance becomes fragmented and difficult to verify. This creates significant risks in a highly regulated environment where non-compliance can result in:

• Substantial financial penalties
• Reputational damage
• Potential patient harm
• Regulatory enforcement actions

Inconsistent Governance and Ethics

Disconnected AI initiatives can lead to inconsistent governance practices and ethical standards, raising concerns about bias, fairness, and transparency in healthcare AI applications.

Higher Total Cost of Ownership

A fragmented AI ecosystem requires repeated investments in tools, personnel, training, and infrastructure. Over time, these costs can grow significantly higher than investing in a robust, centralized platform from the start.

Scalability Challenges

AI projects might start small, but as they grow, data ingestion, storage, and processing demands can explode. Solutions not designed with scalability in mind can lead to performance bottlenecks and unpredictable system behavior.

Approaches to Platform Implementation

When deciding on how to establish an AI platform, healthcare organizations have three primary approaches, each with their own considerations:

Building In-House

In-house development gives healthcare organizations complete control over system design and customization. This approach allows for deep integration with existing internal systems and the potential for highly specialized solutions tailored to unique healthcare workflows.

Organizations with specific requirements not addressed by commercial solutions often gravitate toward this approach. For example, academic medical centers with robust research departments or large health systems with specialized data needs may benefit from building custom platforms.

Building in-house requires substantial investment in specialized talent — data engineers, cloud architects, MLOps specialists, and security experts. While the upfront development costs are high, organizations gain intellectual property ownership and avoid recurring licensing fees of commercial platforms.

The time-to-market is typically longer with built solutions, as teams must design the architecture, develop components, and establish governance frameworks from scratch. However, this investment can pay dividends through perfect alignment with organizational needs and processes.

Organizations choosing this path should be prepared for the ongoing maintenance burden and need for continuous platform evolution as AI technologies advance. Without dedicated teams to maintain and enhance the platform, technical debt can accumulate quickly.

Key consideration: This approach works best for organizations with strong technical capabilities, specialized requirements, and the resources to support ongoing platform development.

Buying a Commercial Platform

Commercial off-the-shelf platforms offer healthcare organizations faster deployment and time-to-value. These solutions come with vendor support, established best practices, and regular updates that keep pace with evolving AI technologies and regulatory requirements.

Many healthcare organizations choose this path to leverage vendor expertise rather than building internal capabilities from scratch. This approach significantly reduces the internal resource burden for platform maintenance and updates.

Leading healthcare AI platforms typically include pre-built connectors for common healthcare systems (EHRs, PACS, etc.), compliance features designed specifically for healthcare regulatory requirements, and workflow tools tailored to clinical environments.

A key limitation is reduced customizability — organizations must adapt their processes to the platform rather than the other way around. Additionally, proprietary platforms may create vendor lock-in, making it difficult to transition to alternative solutions in the future.

Costs typically include subscription fees based on usage metrics, implementation services, and ongoing support. While the initial implementation may be faster than building in-house, integrating the platform with existing systems can still present challenges.

Key consideration: This approach works well for organizations seeking faster implementation, predictable costs, and the ability to leverage established best practices without maintaining extensive technical teams.

Best-of-Breed (Hybrid Approach)

The best-of-breed approach combines custom-built components with commercial solutions to create a tailored platform ecosystem. Organizations can select specialized tools for specific functions — such as imaging AI, natural language processing, or population health analytics — while maintaining a cohesive architecture.

This approach offers greater flexibility than a purely commercial solution while avoiding the resource intensity of building everything in-house. Healthcare organizations can leverage their internal expertise for differentiating capabilities while using established tools for common functions.

A successful best-of-breed implementation requires strong architectural governance and well-defined integration standards. Without careful planning, organizations risk creating a complex patchwork of tools that don’t work together effectively.

The integration complexity can be significant — different tools might not seamlessly align, requiring custom connectors and data transformation processes. This approach demands strong data governance and architecture expertise to prevent the creation of new silos.

Key consideration: This approach is ideal for organizations with some technical capabilities that need to balance customization with implementation speed, particularly when specific components require specialized capabilities not available in all-in-one platforms.

Practical Steps to Get Started

1. Assess Organizational Needs

Identify the types of AI use cases your organization wants to support — clinical decision support, operational forecasting, imaging analytics, etc. Map these to your existing data sources and regulatory requirements.

2. Define Your Approach

Based on your organization’s capabilities, timeline, and specific needs:

• If you have specialized needs and resources, consider building an in-house platform
• If speed is the priority, look for reputable commercial platforms with proven healthcare credentials
• If you need flexibility for certain components, adopt a best-of-breed or hybrid model

3. Engage Cross-Functional Stakeholders

AI success in healthcare depends on alignment across clinical, operational, and technical teams. Involving all stakeholders early ensures the platform meets real needs and complies with internal and external mandates.

4. Plan for Integration & Governance

Draft robust data governance, security, and compliance frameworks. Ensure your platform can integrate easily with EHR systems, laboratory information systems, and external data sources.

5. Iterate & Evolve

Start with a few high-impact AI projects to demonstrate value, then expand use cases. Continuously gather feedback from end-users and refine your platform capabilities.

Conclusion

A platform approach is rapidly becoming the gold standard for AI in healthcare. It helps streamline data management, maintain compliance, foster collaboration, and ultimately deliver more value from AI investments.

The healthcare industry’s unique challenges — strict regulations, sensitive patient data, complex workflows, and high stakes for errors — make a cohesive AI strategy particularly critical. Organizations that implement a unified platform approach position themselves to navigate these challenges while maximizing AI’s potential benefits.

Whether you choose to build a platform from scratch, purchase a commercial offering, or adopt a hybrid best-of-breed model, the key is to establish an adaptable, scalable foundation for innovation. By recognizing the pitfalls of ad hoc AI deployments and investing in a robust platform strategy, healthcare organizations can accelerate the development of transformative AI solutions — enabling better patient outcomes, optimized operations, and a competitive edge in a rapidly evolving landscape.

Disclosure: This content was created through collaboration between human expertise and AI assistance. AI tools contributed to the research, writing, and editing process, while human oversight guided the final content.